Review of medical data analysis based on spiking neural networks
It provides a review for researchers in medical AI, but is incremental as it summarizes existing work without introducing new methods.
This paper reviews recent research on using spiking neural networks (SNNs) for medical data analysis, such as EEG, ECG, EMG signals, and MRI images, to address high energy consumption and latency issues in traditional neural networks, aiming to improve efficiency and accuracy in disease diagnosis.
Medical data mainly includes various types of biomedical signals and medical images, which can be used by professional doctors to make judgments on patients' health conditions. However, the interpretation of medical data requires a lot of human cost and there may be misjudgments, so many scholars use neural networks and deep learning to classify and study medical data, which can improve the efficiency and accuracy of doctors and detect diseases early for early diagnosis, etc. Therefore, it has a wide range of application prospects. However, traditional neural networks have disadvantages such as high energy consumption and high latency (slow computation speed). This paper presents recent research on signal classification and disease diagnosis based on a third-generation neural network, the spiking neuron network, using medical data including EEG signals, ECG signals, EMG signals and MRI images. The advantages and disadvantages of pulsed neural networks compared with traditional networks are summarized and its development orientation in the future is prospected.